AgMERRA and AgCFSR Climate Forcing Datasets for Agricultural Modeling
The AgMERRA and AgCFSR climate forcing datasets were created as an element of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to provide consistent, daily time series over the 1980-2010 period with global coverage of climate variables required for agricultural models. These datasets were designed to be useful for AgMIP's coordinated, protocol-based studies of agricultural impacts ranging from biophysical process studies to global agricultural economic models (Rosenzweig et al., 2013). These datasets are provided to promote consistency and transparency in climate data and to facilitate more harmonized comparisons across regions and between models, particularly in instances where there exist strong market linkages between regions. The 1980-2010 time period is of particular importance for agricultural modeling efforts due to the necessity to calibrate models for improved intercomparison as well as for acting as a baseline upon which future climate scenarios can be statistically and dynamically constructed.
Figure 1: Mean annual precipitation (mm/year) for the 1980-2010 period from AgMERRA. (from Ruane et al., 2015)
AgMERRA and AgCFSR (described in Ruane et al., 2015) follow the example of other climate forcing datasets created by the hydrologic modeling community (e.g., Sheffield et al., 2006; Weedon et al., 2011), but were designed giving careful consideration to agricultural areas and the climatic factors known to be critical for crop development (e.g., mean growing season biases, seasonal cycles, interannual variability, and sub-seasonal extremes) while also focusing on reducing biases of greater importance to agricultural production (e.g., daily precipitation distributions and solar radiation). These datasets are produced by combining state-of-the-art reanalyses (NASA's Modern-Era Retrospective analysis for Research and Applications, MERRA , Rienecker et al., 2011; and NCEP's Climate Forecast System Reanalysis, CFSR, Saha et al., 2010) with observational datasets from in situ observational networks and satellites (as summarized in the table below).
The datasets are stored at 0.25°×0.25° horizontal resolution (~25km), with global coverage and daily values from 1980-2010 in order to form a "current period" climatology. As an example, Figure 1 provides the mean annual precipitation from AgMERRA. Note that some variables do not vary or are simply interpolated below a more coarse effective resolution. Also note that some data have been compressed (or "packed") by use of scaling factors and offsets as indicated in dataset metadata; these must be accounted for when reading the data.
AgMERRA's incorporation of the MERRA-Land product provides substantial improvements in the resolution of the daily precipitation distribution and precipitation extremes over other climate forcing datasets, and both AgCFSR and AgMERRA utilize the NASA/GEWEX Solar Radiation Budget data for improved solar radiation values (see Ruane et al., 2015). In addition, AgMERRA and AgCFSR provide relative humidity at the time of maximum daily temperature in order to better gauge maximum evapotranspiration in agricultural applications.
AgMERRA and AgCFSR were created to be used explicitly for research purposes with uses for impacts analysis and scenario generation, but it is important to recognize that they do not represent a climate observational record. These datasets may not be suitable for other applications, including including, but not limited to, detailed trend analysis or energy and water budget evaluation because they blend datasets and rely on reanalyses with changing inputs between 1980 and 2010.
These datasets are provided "as is", and careful application of these datasets should be employed with appropriate and critical rigor. Publications deriving from the use of these datasets should cite the Ruane et al. (2015) reference below. Should you encounter any bugs or difficulties or have any suggestions or recommendations, please contact Dr. Alexander Ruane.
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Methodology
The table below summarizes variable construction methodologies for the AgMERRA and AgCFSR climate forcing datasets. The effective resolution for temperature and radiation is higher than the 0.25°×0.25° resolution of the climate forcing datasets if there is heavy reliance on a particular observational dataset. DTR stands for Diurnal temperature Range.
| Variable | Eff. Res. | AgMERRA Construction Summary | AgCFSR Construction Summary |
|---|---|---|---|
| Mean, Min and Max Temperature (°C) | 0.5° | Mean: MERRA daily Tmax and Tmin values shifted by average monthly temperature correction from CRU and WM for each month in each year on 0.5° grid. DTR: Adjusted to be 0.75 of the way between MERRA and CRU DTRs. Ensure that Tmax > Tmin. | Mean: CFSR daily Tmax and Tmin values shifted by average monthly temperature correction from CRU and WM for each month in each year on 0.5-degree grid. DTR: Adjusted to be equivalent to CRU DTR. Ensure that Tmax > Tmin. |
| Precipitation (mm/day) | 0.25° | Wet days: Average of MERRA-Land and CRU wet days for each month in each year. Mean: MERRA-Land daily values multiplied by correction factor imposing mean of CRU, GPCC, and WM for each month and each year at 0.5 resolution. 0.25° detail imposed from average monthly spatial pattern drawn from ensemble of TRMM, CMORPH, and PERSIANN. | Wet days: CRU wet days for each month in each year. Mean: MERRA-Land daily values multiplied by correction factor imposing mean of CRU, GPCC, and WM for each month and each year at 0.5 resolution. 0.25° detail imposed from average monthly spatial pattern drawn from ensemble of TRMM, CMORPH, and PERSIANN. |
| Solar Radiation (MJ/m2/day) | 1.0° | 07/1983-12/2007: NASA/GEWEX SRB data linearly interpolated to 0.25-degree grid. 01/1980-06/1983 and 01/2007-12/2010: MERRA downward shortwave flux corrected using quantile-mapping and the statistics of SRB Beta distribution. | 07/1983-12/2007: NASA/GEWEX SRB data linearly interpolated to 0.25-degree grid. 01/1980-06/1983 and 01/2007-12/2010: CFSR downward shortwave flux corrected using quantile-mapping and the statistics of SRB Beta distribution. |
| Relative Humidity at Time of Max Temp (%) | 0.25° | Calculated from MERRA specific humidity, maximum temperature, and surface pressure and then linearly interpolated to 0.25-degree grid. | Calculated from CFSR specific humidity, maximum temperature, and surface pressure and then linearly interpolated to 0.25-degree grid. |
| Wind Speed (m/s) | 0.25° | MERRA wind speeds linearly interpolated to 0.25-degree grid. | Adjusted CFSR 10-m wind speeds to 2-m velocities and then linearly interpolated to 0.25-degree grid. |
Updates
Aug. 18, 2014: AgMERRA and AgCFSR precipitation datasets have been updated (denoted as version 1.1) with the following changes:
- AgMERRA_YYYY_prate.nc4 and AgCFSR_YYYY_prate.nc4 now use a different scaling factor. This corrects a bug in which very heavy precipitation events (daily rainfall > ~400mm/day) were marked as missing.
- Corrects a bug in which some extreme coastal points were assigned only 1 rainy day per month for 2009-2010 in AgCFSR and only approximately one half of the desired number of rainy days per month in AgMERRA.
July 20, 2017: Please note that AgMERRA precipitation over Guinea and Sierra Leone (and portions of Liberia) is erroneously low for 2010 while apparently fine for 1980-2009. We are working to track down this 2010 error in this region and will update in the next version of AgMERRA (thanks to Matthew Fulakeza at NASA GISS for pointing this out).
References
Rienecker, M. R. et al., 2011: MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.
Rosenzweig, C., et al., 2013: The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies, Agr. Forest Meteorol., 170, 166-182, doi:dx.doi.org/10.1016/j.agrformet.2012.09.011.
Ruane, A.C., R. Goldberg, and J. Chryssanthacopoulos, 2015: AgMIP climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation, Agr. Forest Meteorol., 200, 233-248, doi:10.1016/j.agrformet.2014.09.016.
Saha, S., et al., 2010: The NCEP Climate Forecast System Reanalysis, Bull. Amer. Meteorol. Soc., 91, 1015-1057, doi:10.1175/2010BAMS3001.1.
Sheffield, J., G. Goteti, and E.F. Wood, 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19, 3088-3111, doi:10.1175/JCLI3790.1.
Weedon, G.P., et al., 2011: Creation of the WATCH Forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century, J. Hydrometeorol., 12, 823-848, doi:10.1175/2011JHM1369.1.
Contact
Please address all queries about these datasets to Dr. Alexander Ruane.
